Python is quickly becoming the most widely used programming language today. Some of the reasons being how easy to read and versatile it is. It is a language that can be used in different fields. Some include Machine Learning, Data Analysis, Artificial Intelligence. In this article, we will discuss some of the top Python programming language libraries for Data Analysis in 2022.
NUMPY
Firstly, Numpy. This is a library for numerical analysis with Python. It provides powerful, fast, and efficient operations on robust data. It is the best library when working with numbers and N-dimensional arrays. Simply install Numpy by running the code” import numpy as np“. Some other ways of using Numpy are listed in the image below.
PANDAS
Pandas is the most preferred library to analyze, transform and manipulate data. It provides data structures and operations for working with tabular data. Pandas is used to import, prepare and data. Its eloquent syntax gives the freedom to deal with missing data.
For example, the image below is an illustration of the use of pandas. We imported a datasheet called “names.csv” by calling the import pandas command. that is, “import pandas as pd”. (pd being our alias, you can name it however you want). We also went ahead to read the data from the data source into a pandas Dataframe. Lastly, the data command displayed our datasheet.
The next image includes a list of 8 different built in functions of Pandas in Python.
MATPLOTLIB
This is a library for plotting and visualising data in Python. We can create line-graphs, bar chats, scatter plots, histograms, pie charts with this library. In other words, it is used for its ability to produce publication-quality plots. It also provides an object-oriented API, which can be used to embed those plots into applications. To install, simply enter the code “pip install matplotlib” into terminal.
With a few lines of code, we generated a basic plot using python matplotlib as seen below. In order to import matplotlib, we run the ‘import matplotlib.pyplot as plt‘ command. This reason being matplotlib itself is a very vast library and we want to import only a specific module, in this case the pyplot module. the next line of code “%matplot lib inline” is basically a command that we give matplotlib to make sure all the graphs and charts we create stay inline and within the notebook. We added title, labels (Side X) (Side Y) and colors to our graph to bring in more detail to it.
SEABORN
Lastly, this is another library built upon Matplotlib that is used for creating data visuals. It is a very popular library for machine learning that includes a vast collection of machine learning algorithms. While also being used for making predictions, one of its benefits is to make it easier to produce attractive, publication-quality graphs. It has four mandatory dependencies which are Pandas, Numpy, Matplotlib and Scipy.
To learn more on Python, check out Derek Banas on Youtube, Freedcodecamp (Youtube), Programming with Mosh (Youtube) and online documentations of each library.
CONCLUSION
So there you have it, I have reviewed my personal favorite top python libraries for data analysis in 2022. Although there are thousands of other libraries, this post was written in order to give you an introduction to the most common ones and their functions. These libraries are highly useful, easily adoptable by anyone, and with a great open source online documentation community behind each of them.
